In the industrial fields of semiconductors and other related products, there have been demands for more accurate measurements of micropattern shapes along with the progress of microfabrication in pattern processing sizes. In addition to those pattern sizes, it has also been required to evaluate the microroughness of pattern edges (edge roughness) that occur at random. Particularly, in the case of semiconductor processes, it has been found that device performance is affected significantly by local fluctuation of line widths, that is, line width roughness caused by such edge roughness on gates and wiring patterns, that is, line edge roughness or edge roughness appearing on both sides of lines. Furthermore, high precision measurements have also been required for size deviations of holes and dot patterns, as well as for the roughness of pattern edges.
A size (length) means a distance between two points (or two lines) on a user-specified sample. The degree of pattern edge roughness is usually represented by a standard deviation σ of the distribution of deviations from an ideal approximated shape of edge points (an approximated line calculated from a set of edge points in the case of a line edge) or its three-times value (3σ). Hereunder, unless otherwise specially noted, the above-described general 3σ will be referred to simply as “roughness” denoting a roughness evaluation index. The above descriptions can also apply to the difference between the maximum and minimum values of deviation from an ideally approximated shape, as well as to the average (deviation average) of absolute values of deviation, in addition to the integer multiples of the standard deviation σ.
When realizing the above described high precision size (length) measurement and high precision roughness measurement, what is the most important issue is removing noise influences. And when observing patterns with the use of a scanning microscope or visualizing the two-dimensional distribution data of the signal intensity obtained as a result of such observations (hereinafter, these will be described as observed images collectively), the images always include random noises, which affect the size (length) and roughness measurements.
There are two types of such random noise influences: influences on size (length) measurements and influences online width measurements. The influences on the size (length) measurements appear as variations of measured values and influences on line width measurements appear as observation of values larger than the true values, since noises outside the object line are apt to appear in signal profiles in the line width direction of secondary electron particles. Furthermore, according to the Proceedings of SPIE, Vol. 5752, pages 480 to 488, when there are many random noises, measured values come to increase in roughness measurements, and in the worst case, the measurement itself is disabled.
In order to minimize such noise influences to a negligible level, there have been considered three methods. The first method is integrating signals (this means observing signals for a long time to obtain a signal intensity that can have a satisfactorily high S/N ratio). The second method is averaging signals in a direction parallel to pattern edges of observed images (hereinafter, to be described as averaging). The third method is averaging signals in a direction vertical to the edges of observed images (hereinafter, to be described as smoothing). In the case of a non-line pattern, the second method carries out the averaging in a direction of an object edge (or in a tangential direction at each pattern edge point) and the third method carries out the averaging in a direction vertical to an object edge (or in a tangential direction at each pattern edge point).
Instead of the merit of noise reduction, however, those three methods come to have the following demerits.
The first method includes such demerits as an increase of inspection time (that lowers the production throughput) and sticking carbon, etc. on the observation spots due to irradiation for a long time of an electron beam, and pattern deformation (mainly shrinkage).
A demerit of the second to third methods is loss of information through the averaging. Because the averaging is considered to be equivalent to the vignette of images, part of the information included in the original two-dimensional distribution data might be lost through such averaging/smoothing. For example, in the case of a line pattern, if such averaging is applied to a pattern in a direction parallel to the pattern edges, the short period component of the roughness in the longitudinal direction of the pattern is lost. In the case of size (length) measurements, because it is just required to know the average value of sizes (lengths) within a comparatively long range in the longitudinal direction of the pattern, even when the short period component of the roughness is lost through such averaging, it does not cause any serious problems. In the case of roughness measurements, however, values to be obtained become smaller than their true ones, and thereby the reliability of the measured values comes to be lost. Consequently, it becomes difficult to detect a difference of roughness between two patterns, and thereby the size determination cannot be made accurately.
Other demerits of the third method are changes of size and roughness values due to the broadening of signal profiles (details will be described later) (broadening of the secondary electrons intensity distribution in a direction vertical to an object pattern edge). Consequently, in any of the size (length) and roughness measurements, the third method is unable to make determinations accurately for the size difference between two patterns just like the demerit mentioned above.
Conventionally, in order to remove noise influences, all the above-described three methods have been combined. In recent years, however, there have often been used samples of which resistance to irradiation of a charged electron beam is weak as objects of such size (length) and roughness measurements. In such cases, the deformation of patterns, which is a demerit of the first method, comes to arise as a problem. And in order to solve this problem, it has been required to reduce the number of signal integrations to as little as possible to obtain images to be observed. As a result, the image processings of the second and third methods have come to be considered more as important means, and accordingly the side effect of those methods have also come up as a problem.
At present, in the case of size (length) measurements, it is negligible that high frequency components in the pattern edge fluctuation are lost. So, the second method is employed to average a long region formed along an object edge, thereby reducing noise influences. On the other hand, in the case of roughness measurements, for example, the Proceedings of SPIE, Vol. 5752, pages 480 to 488 discloses a method that obtains the same pattern image twice to process the image data. JP-A No. 2006-215020 also discloses a new evaluation method that estimates a noise component included in a roughness measured value by premising existence of the characteristics of the noise and those of the edge roughness to be observed, thereby estimating a roughness measured value while there is no noise without using the first and second methods. Using these techniques could successfully avoid the demerit of the first method, which is damage to the observation samples, and the demerit of the second method, which is the loss of the high frequency components.